U.S. patent number 11,436,237 [Application Number 17/125,935] was granted by the patent office on 2022-09-06 for ranking datasets based on data attributes.
This patent grant is currently assigned to International Business Machines Corporation. The grantee listed for this patent is International Business Machines Corporation. Invention is credited to Poornima Iyengar, Kalapriya Kannan, Manjit Singh Sodhi.
United States Patent |
11,436,237 |
Sodhi , et al. |
September 6, 2022 |
Ranking datasets based on data attributes
Abstract
Ranking a group of datasets using a computer includes
determining a set of target data fields from a set of process
documents that indicate user data field preferences. A set of
target dataset attributes from a set of data use documents indicate
user data scope preferences. A plurality of metadata sets for an
associated plurality of datasets the computer determines having a
field suitability value exceeding a predetermined suitability
threshold value. The FSV represents a degree of similarity between
a set of fields associated with said dataset and the set of target
data fields. The computer assesses metadata sets with regard to the
target attributes and generates a compared attribute score for each
candidate dataset. A degree of likelihood is indicated that an
associated dataset will have content exhibiting said target dataset
attributes. The computer candidate datasets is based on the
compared attribute score.
Inventors: |
Sodhi; Manjit Singh (Bangalore,
IN), Kannan; Kalapriya (Bangalore, IN),
Iyengar; Poornima (Bangalore, IN) |
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Assignee: |
International Business Machines
Corporation (Armonk, NY)
|
Family
ID: |
1000006542966 |
Appl.
No.: |
17/125,935 |
Filed: |
December 17, 2020 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20220197914 A1 |
Jun 23, 2022 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
7/08 (20130101); G06F 16/24578 (20190101) |
Current International
Class: |
G06F
7/08 (20060101); G06F 16/2457 (20190101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Chmielinski, et al., "The Data Nutrition Project", .COPYRGT. 2018,
last printed Dec. 16, 2020, 18 pages,
<https://datanutrition.org/>. cited by applicant .
Gebru, et al., "Datasheets for Datasets", arXiv:1803.09010v7, Mar.
19, 2020, 24 pages, <https://arxiv.org/abs/1803.09010>. cited
by applicant .
Holland, et al., "The Dataset Nutrition Label: A Framework to Drive
Higher Data Quality Standards", arXiv: 1805.03677, Draft May 2018,
21 pages, <https://arxiv.org/abs/1805.03677>. cited by
applicant .
Mell, et al., "The NIST Definition of Cloud Computing", National
Institute of Standards and Technology, Special Publication 800-145,
Sep. 2011, 7 pages. cited by applicant .
Mitchell, et al., "Model Cards for Model Reporting", arXiv:
1810.03993v2, Jan. 14, 2019, 10 pages,
<https://arxiv.org/abs/1810.03993>. cited by
applicant.
|
Primary Examiner: Beausoliel, Jr.; Robert W
Assistant Examiner: Santos; Pedro J
Attorney, Agent or Firm: Petrocelli; Michael A.
Claims
What is claimed is:
1. A computer implemented method to sort a plurality of datasets
according to dataset attributes, comprising: identifying, by a
computer, a set of target data fields from a set of process
documents, said process documents indicating data field preferences
of a user; identifying, by said computer, a set of target dataset
attributes from a set of data use documents, said data use
documents indicating data scope preferences for said user,
attributes include a data property either derived using automation
or added to the set of the target dataset attributes as part of an
input provide by a domain expert; generating, by a computer, a
plurality of metadata sets for an associated plurality of datasets;
determining, by said computer, candidate datasets having a field
suitability value that exceeds a predetermined suitability
threshold value, said field suitability value representing a degree
of similarity between a set of fields associated with said dataset
and the set of target data fields; assessing, by said computer, the
associated metadata set for each candidate dataset, with regard to
the target attributes and generating, by said computer, a compared
attribute score for each candidate dataset, indicating a degree of
likelihood that an associated dataset will have content exhibiting
said target dataset attributes; and generating, by said computer, a
list of said candidate datasets sorted by said compared attribute
scores.
2. The method of claim 1, wherein said data use documents include
information in a format selected from a list consisting of Business
Process Execution Language (BEPL), and Unified Modeling Language
(UML).
3. The method of claim 1, wherein said data target attributes are
extracted from elements of said process documents, selected from a
list consisting of class diagrams, activity diagrams, sequence
diagrams, and component diagrams.
4. The method of claim 1, further including designating a candidate
dataset having a highest compared attribute score as a selected
dataset.
5. The method of claim 4, further including establishing a set of
search parameters for a search to be conducted on said selected
dataset; and updating a historic use field in the metadata set
associated with a dataset selected for searching with a search
context value that represent aspects of the search parameters.
6. The method of claim 5, wherein said ranking is based, at least
in part, on the historic use field values.
7. The method of claim 1, wherein said compared attribute scores
are based, at least in part on an associated desirability value
associated with each of said target dataset attributes.
8. The method of claim 1, wherein said sets of metadata include
information selected from a list consisting of: domain, gender, age
group, geographic distribution, demographic distribution,
statistical ranges of numerical values, and context of
applicability.
9. system to sort a plurality of datasets according to dataset
attributes, which comprises: a computer system comprising a
computer readable storage medium having program instructions
embodied therewith, the program instructions executable by a
computer to cause the computer to: identify a set of target data
fields from a set of process documents, said process documents
indicating data field preferences of a user; identify a set of
target dataset attributes from a set of data use documents, said
data use documents indicating data scope preferences for said user,
attributes include a data property either derived using automation
or added to the set of the target dataset attributes as part of an
input provide by a domain expert; generate a plurality of metadata
sets for an associated plurality of datasets; determine candidate
datasets having a field suitability value that exceeds a
predetermined suitability threshold value, said field suitability
value representing a degree of similarity between a set of fields
associated with said dataset and the set of target data fields;
assess the associated metadata set for each candidate dataset, with
regard to the target attributes and generating, by said computer, a
compared attribute score for each candidate dataset, indicating a
degree of likelihood that an associated dataset will have content
exhibiting said target dataset attributes; and generate a list of
said candidate datasets sorted by said compared attribute
scores.
10. The system of claim 9, wherein said data use documents include
information in a format selected from a list consisting of Business
Process Execution Language (BEPL), and Unified Modeling Language
(UML).
11. The system of claim 9, wherein said data target attributes are
extracted from elements of said process documents, selected from a
list consisting of class diagrams, activity diagrams, sequence
diagrams, and component diagrams.
12. The system of claim 9, further including instructions for the
computer to designate a candidate dataset having a highest compared
attribute score as a selected dataset.
13. The system of claim 12, further including instructions for the
computer to establish a set of search parameters for a search to be
conducted on said selected dataset; and to update a historic use
field in the metadata set associated with a dataset selected for
searching with a search context value that represent aspects of the
search parameters.
14. The system of claim 13, wherein said ranking is based, at least
in part, on the historic use field values.
15. The system of claim 9, wherein said compared attribute scores
are based, at least in part on an associated desirability value
associated with each of said target dataset attributes.
16. The system of claim 9, wherein said sets of metadata include
information selected from a list consisting of: domain, gender, age
group, geographic distribution, demographic distribution,
statistical ranges of numerical values, and context of
applicability.
17. computer program product to sort a plurality of datasets
according to dataset attributes, the computer program product
comprising a computer readable storage medium having program
instructions embodied therewith, the program instructions
executable by a computer to cause the computer to: identify, using
a computer, a set of target data fields from a set of process
documents, said process documents indicating data field preferences
of a user; identify, using a computer, a set of target dataset
attributes from a set of data use documents, said data use
documents indicating data scope preferences for said user,
attributes include a data property either derived using automation
or added to the set of the target dataset attributes as part of an
input provide by a domain expert; generate, using a computer, a
plurality of metadata sets for an associated plurality of datasets;
determine, using a computer, candidate datasets having a field
suitability value that exceeds a predetermined suitability
threshold value, said field suitability value representing a degree
of similarity between a set of fields associated with said dataset
and the set of target data fields; assess, using a computer, the
associated metadata set for each candidate dataset, with regard to
the target attributes and generating, by said computer, a compared
attribute score for each candidate dataset, indicating a degree of
likelihood that an associated dataset will have content exhibiting
said target dataset attributes; and generate, using a computer, a
list of said candidate datasets sorted by said compared attribute
scores.
18. The computer program product of claim 17, wherein said data use
documents include information in a format selected from a list
consisting of Business Process Execution Language (BEPL), and
Unified Modeling Language (UML).
19. The computer program product of claim 17, wherein said data
target attributes are extracted from elements of said process
documents, selected from a list consisting of class diagrams,
activity diagrams, sequence diagrams, and component diagrams.
20. The computer program product of claim 17, further including
instructions for the computer to designate a candidate dataset
having a highest compared attribute score as a selected dataset.
Description
BACKGROUND
The present invention relates generally to the field of dataset
analysis and, more particularly, to computer dataset
evaluation.
Datasets are groups of data that can be used by various computer
systems to provide answers to questions about many real-world and
simulated situations. Often, datasets include information about
past transactions or other historic information from which
predictions about similar current and future transactions may be
made. In some domains, datasets are produced by user systems as a
byproduct of system operation and kept future use. In other
domains, datasets, especially large or customized datasets, may be
provided by third parties at an expense to the user. Artificial
Intelligence (AI) systems can identify patterns within data
contained in datasets to reveal trends that are often difficult to
predict in other ways. Since datasets can vary widely in terms of
content, some datasets will be more useful to certain users than
others.
The value of a dataset can vary from use case to use case. It is
possible to evaluate the worth of datasets and to rank evaluated
datasets if an intended use of the data is known.
SUMMARY
According to one embodiment, a computer-implemented method to sort
a plurality of datasets according to dataset attributes, includes
identifying, by a computer, a set of target data fields from a set
of process documents, the process documents indicating data field
preferences of a user. The computer identifies a set of target
dataset attributes from a set of data use documents, and the data
use documents indicate data scope preferences for the user. The
computer generates a group of metadata sets for an associated
plurality of datasets. The computer determines candidate datasets
having a field suitability value that exceeds a predetermined
suitability threshold value, and the field suitability value
represents a degree of similarity between a set of fields
associated with said dataset and the set of target data fields. The
computer assesses the associated metadata set for each candidate
dataset, with regard to the target attributes. The computer
generates a compared attribute score for each candidate dataset
that indicates a degree of likelihood that an associated dataset
will have content exhibiting said target dataset attributes. The
computer generates a list of said candidate datasets sorted by the
compared attribute scores.
According to aspects of the invention, the data use documents
include information in a format selected from a list consisting of
Business Process Execution Language (BEPL), and Unified Modeling
Language (UML). According to aspects of the invention, the data
target attributes are extracted from elements of said process
documents, selected from a list consisting of class diagrams,
activity diagrams, sequence diagrams, and component diagrams.
According to aspects of the invention, a candidate dataset having a
highest compared attribute score is designated as a selected
dataset. According to aspects of the invention, establishing a set
of search parameters for a search to be conducted on said selected
dataset; and updating a historic use field in the metadata set
associated a dataset selected for searching with a search context
value that represent aspects of the search parameters. According to
aspects of the invention, the ranking is based, at least in part,
on the historic use field values. According to aspects of the
invention, the compared attribute scores are based, at least in
part on an associated desirability value associated with each of
said target dataset attributes. According to aspects of the
invention, the sets of metadata include information selected from a
list consisting of: domain, gender, age group, geographic
distribution, demographic distribution, statistical ranges of
numerical values, and context of applicability.
According to another embodiment a system to rank a plurality of
datasets, which comprises: a computer system comprising a computer
readable storage medium having program instructions embodied
therewith, the program instructions executable by a computer to
cause the computer to: identify a set of target data fields from a
set of process documents, said process documents indicating data
field preferences of a user; identify a set of target dataset
attributes from a set of data use documents, said data use
documents indicating data scope preferences for said user; generate
a plurality of metadata sets for an associated plurality of
datasets; determine candidate datasets having a field suitability
value that exceeds a predetermined suitability threshold value,
said field suitability value representing a degree of similarity
between a set of fields associated with said dataset and the set of
target data fields; assess the associated metadata set for each
candidate dataset, with regard to the target attributes and
generating, by said computer, a compared attribute score for each
candidate dataset, indicating a degree of likelihood that an
associated dataset will have content exhibiting said target dataset
attributes; and generate a list of said candidate datasets sorted
by said compared attribute scores.
According to another embodiment, a computer program product to rank
a plurality of datasets, the computer program product comprising a
computer readable storage medium having program instructions
embodied therewith, the program instructions executable by a
computer to cause the computer to: identify, using a computer, a
set of data target attributes from a set of process documents that
indicate the data field preferences of a user; identify, using said
computer, a set of dataset target attributes from a set of data use
documents that indicate data scope preferences of the user;
generate, using said computer, a plurality of metadata sets for an
associated plurality of datasets; determine, using said computer, a
top-k candidate datasets having a field suitability value that
exceeds a predetermined suitability threshold value; assess, using
said computer, the associated metadata set for each candidate
dataset, with regard to the target attributes and generating, by
said computer, a compared attribute score for each candidate
dataset; and rank, using said computer, said candidate datasets
based, at least in part, on said compared attribute score.
The value of a given dataset can be based on a variety of factors,
including dataset record field content and the scope of the
information contained. For example, many data analysis systems
require certain kinds of information (e.g., certain fields) in
order to provide meaningful output, and datasets with higher
amounts of suitable information (e.g., the higher number of desired
data fields) are preferred over datasets with fewer required data
fields. Similarly, data analysis systems need data that is suited
to the questions being presented to the system in order to provide
meaningful output, and the more relevant a given dataset is to an
intended scope of use (e.g., anticipated questions to be asked),
the higher the dataset value.
Aspects of the invention match data requirements (including Target
Data Fields and Target Dataset Attributes) of a user, including
those with business applications should be matched against metadata
derived from the data in the datasets. According to aspects of the
invention, the metadata should represent the dataset content,
describing data content demographics and statistical properties of
the data content.
Aspects of the invention relate the fields of the data with
meanings through a variety of methods, including ontology use and
key value pair use.
Aspects of the invention first select a group of provide a score
for the data set based on the target dataset requirements and its
matching to the meta-data by which business can evaluate which data
set is more suited for their requirements.
According to aspects of invention, the derived metadata includes:
Statistical properties (e.g., the type of distribution, mean,
variance and related properties, any correlations; and whether it
has time-series data); The various fields and their related
meanings/semantics (e.g., in a loan approval dataset, "spouse" is
similar to "wife" and "husband"; if a ".CSV" file and the
associated schema is known, then various meanings (e.g., fields
related to opening new marketing channels might have certain
meanings that are different than similarly-named fields used to
identify fields associated with sporting events) suited to the
schema can be recorded as metadata; when used in accordance with
consent and permission granted by an individual identified,
Personally Identifiable Information (e.g., emails, phone numbers,
addresses/contact details); fields related to previous dataset use
(e.g., via historical mining of the data set usage and identifying
other data sets with which it has been used); derived metadata also
includes information about the content representations like domain,
gender, age group, geographic distribution (this can indicate that
a dataset is applicable for certain age groups, the banking domain,
or for certain regions, etc.).
Aspects of the invention determine a dataset a value based on
dataset content (e.g., as characterized by dataset metadata).
According to aspect aspects of the invention metadata includes
descriptive information indicating content-based characteristics of
the dataset. Aspects of the invention identify the data
requirements of the businesses. Aspects of the invention rank
datasets and provide relevance scores based on attributes and range
values for each of the metadata and its range of values. Aspects of
the invention formulate and derive a systematic method for
determining a value for the dataset based on the business
requirements and the content of the data. Aspects of the invention
use the score and derive a ranking for each facets of the metadata.
Aspects of the invention use the dataset value to compare two data
sets with respect to a business requirement. Aspects of the
invention enable a search mechanism of the data sets based on the
content of the data. Aspects of the invention use the history of
data usage in different contexts to generate metadata and use them
to identify business context when search events are conducted.
Aspects of the invention searches a corpus of data sets based on
the input of a set of business requirements; and ranks the results
in terms of those best matched for suitability. According to
aspects of the invention, Target Data Fields (e.g., to support
required business processes are defined through various diagrams
using standard formats (e.g., Business Process Execution Language
(BPEL) which can provide extractable activities, actors,
ordering/sequences; and Unified Modeling Language (UML) that
provides diagrams for relevant software engineering artifacts.)
According to aspects of the invention, UML documents can include
provides class diagrams, activity diagrams, sequence diagrams, and
component diagrams.
According to aspects of the invention, activities from BPEL
diagrams can be matched to a UML activity diagram and used to
extract class level components. According to aspects of the
invention, class level components can give all the requirements of
the fields.
Aspects of the invention can derive Business Requirements. Aspects
of the invention can evaluate datasets and metadata. Aspects of the
invention can rank datasets, data values, and data facets. Aspects
of the invention can help determine if a given dataset is relevant
to providing information regarding how to open a mobile or online
commerce channel for a business, using a questionnaire (or other
data use documents that indicate data requirements).
Since datasets having content with some attributes may be more
useful than others, aspects of the system (including, e.g., user
data requirement questionnaires and other data use documents) help
us identify what a user needs as content. Aspects of the invention
identifies relevant contexts of data, indicating which datasets
would be a good match for various user goals (e.g., launch new tea
product using coupons, etc.).
According to some aspects of the invention, the PDAM includes a
"discovery unit" that locates Activity Diagrams and a Class Diagram
Locator. According to some aspects of the invention, the PDAM
includes an entity extractor and an activity extractor.
According to some aspects of the invention, the term attribute may
be used interchangeably with the word facet. According to aspects
of the invention, the CAAM includes an otology mapping engine.
According to aspects of the invention, the CAAM includes aspects
that determine whether a dataset matches a business need. According
to aspects of the invention, the HULUM 124 includes a data usage
metadata extractor and a dataset historic use log that indicates
historical data set usage and identifies other datasets with which
a selected dataset has been used.
BRIEF DESCRIPTION OF THE DRAWINGS
These and other objects, features and advantages of the present
invention will become apparent from the following detailed
description of illustrative embodiments thereof, which is to be
read in connection with the accompanying drawings. The various
features of the drawings are not to scale as the illustrations are
for clarity in facilitating one skilled in the art in understanding
the invention in conjunction with the detailed description. The
drawings are set forth as below as:
FIG. 1 is a schematic block diagram illustrating an overview of a
system for computer-implemented method to rank a plurality of
datasets in accordance with dataset content and desired data
attributes according to embodiments of the present invention.
FIG. 2 is a flowchart illustrating a method, implemented using the
system shown in FIG. 1, of a system for a computer-implemented
method to rank a plurality of datasets in accordance with the
present invention.
FIG. 3A is an alternate view of aspects of the system shown in FIG.
1.
FIG. 3B is a schematic representation of aspects of the system
shown in FIG. 1 in use to provide a set of ranked datasets,
according to embodiments of the present invention.
FIG. 4 is a schematic overview of the system shown in FIG. 1, with
aspects of the system arranged into multiple stages.
FIG. 5 is an alternate view of the system shown in FIG. 1, with
aspects of the system arranged according to a workflow outline,
including a list of methods and related details.
FIG. 6 is a schematic representation of aspects of "datavalue"
entry and a "dataranking" entry generated according embodiments of
the present invention.
FIG. 7 is an exemplary business data use questionnaire and
associated sample answers according embodiments of the present
invention.
FIG. 8 is a schematic block diagram depicting a computer system
according to an embodiment of the disclosure which may be
incorporated, all or in part, in one or more computers or devices
shown in FIG. 1, and cooperates with the systems and methods shown
in FIG. 1.
FIG. 9 depicts a cloud computing environment according to an
embodiment of the present invention.
FIG. 10 depicts abstraction model layers according to an embodiment
of the present invention.
DETAILED DESCRIPTION
The following description with reference to the accompanying
drawings is provided to assist in a comprehensive understanding of
exemplary embodiments of the invention as defined by the claims and
their equivalents. It includes various specific details to assist
in that understanding but these are to be regarded as merely
exemplary. Accordingly, those of ordinary skill in the art will
recognize that various changes and modifications of the embodiments
described herein can be made without departing from the scope and
spirit of the invention. In addition, descriptions of well-known
functions and constructions may be omitted for clarity and
conciseness.
The terms and words used in the following description and claims
are not limited to the bibliographical meanings, but, are merely
used to enable a clear and consistent understanding of the
invention. Accordingly, it should be apparent to those skilled in
the art that the following description of exemplary embodiments of
the present invention is provided for illustration purpose only and
not for the purpose of limiting the invention as defined by the
appended claims and their equivalents.
It is to be understood that the singular forms "a," "an," and "the"
include plural referents unless the context clearly dictates
otherwise. Thus, for example, reference to "a participant" includes
reference to one or more of such participants unless the context
clearly dictates otherwise.
Now with combined reference to the Figures generally and with
particular reference to FIG. 1 and FIG. 2, an overview of a system
100 for a computer-implemented method to rank a plurality of
datasets in accordance with dataset content as carried out by a
server computer 102 having optionally shared storage 104. With
continued reference to FIG. 1, the server computer is in
communication with a source of Process Documents 106 (e.g., BPEL,
UML diagrams, etc.) that indicate desired dataset data fields. The
server computer 102 includes a Process Documentation Analysis
Module (PDAM) 112 that uses known UML processing and assessment
tools (including diagram identifiers and other similar UML content
extractors) and BPEL readers to review and mine documents to
identify Target Data Fields. These Target Data Fields provide
information about the format of data which is most compatible with
the needs of a given user. As an example, a user may provide
documents written in Business Process Execution Language (BPEL),
and documents in this format can indicate the various activities,
actors, and process sequences that are important to the operation
of the user's business, and these aspects can be extracted to help
understand the data needs of the user. As another example, a user
may provide documents presented using Unified Modeling Language
(UML) or similar modeling language, and documents in this format
can provide insight about the software artifacts important to
operation of the user's processing systems, including class
diagrams, activity diagrams, and sequence diagrams. Activities from
the BPEL documents can be matched to the UML diagrams and used to
extract class level components of the user system. The server
computer 102 uses the extracted class level components to determine
data field requirements.
The server computer 102 is also in communication with a source of
Data Use Documents 108 (e.g., such as data requirement
questionnaires) that indicate desired dataset attributes. The
server computer 102 is also in communication with a source of one
or more datasets 110.
The server computer 102 includes Data Use Document Analysis Module
(DUDAM) 114 to assess data use documents to identify Target Dataset
Attributes. A user may provide information about expected data use
in a variety of ways (e.g., through detailed questionnaire
responses, providing groups of anticipated questions, specifying
topics of interest, etc.), and this information indicates what
scope of data content will be most compatible with the needs of the
user. According to aspects of the invention, this information
provides input regarding business requirements, so that matching
data content may be identified as such when encountered in various
dataset. For example, if a user wants to gain insight about
marketing a particular product in a given region, datasets that
contain information about sales of that product in that region
would likely be more valuable than datasets that only included
sales information of that product in a different region. General
sales information about the product might also be valuable to this
user, and user data use requirement inquiries may be structured to
gather this level of detail, according to aspects of the present
invention. According to aspects of the present invention, a wide
variety of preferences regarding data scope can be collected from a
user in order in train the system 100 regarding user data use
preferences.
The server computer 102 includes Metadata Generation Module (MGM)
116 that uses (e.g., an ontology engine, statistical data
processers, or similar known tools) to extract and generate dataset
attribute identifying metadata. For example, the server computer
102 may use domain specific ontologies that include machine
readable statements about the domain (e.g., describing various
domain concepts and the relationships among them) to assign
meanings for fields in a given dataset. The MGM 116 may also
receive simple key value pairs that indicate field meaning. Other
meaning assignment arrangements chosen in accordance with the
judgment of one skilled in this field may also suffice. The
generated metadata can include many kinds of useful information
about the data contained in a given dataset. Derived metadata may,
for example, include statistical properties of the data such as the
type of distribution contained, mean values, data value variance.
Derived metadata may also include indications of time series data
and other similar and a variety of other data field correlations.
According to aspects of the invention, derived metadata can also
include information (as derived through known data mining
techniques) about how the data has been used previously and what
other inquiries it has supported. Derived metadata may also provide
(when affirmative original content provider consent is confirmed)
personally identifiable information that can be useful for
marketing of products and opening new marketing channels or
otherwise allowable, in accordance with the confirmed consent
provided. Derived metadata may include information about the
content demographic representations found within the data,
including topical domain, and aggregated gender, age group,
geographic distribution, and so forth. The derived metadata of a
given dataset presents a summary of the dataset content and
provides an indication of the data uses for which the dataset is
well-suited. For example, derived metadata may indicate that a
given dataset is well suited to answer questions about a certain
domain, certain demographic ranges, geographically-relevant
questions, and so on. The more well-suited a dataset is for a given
data use, the more value the dataset has for a user with those data
use goals.
The server computer 102 includes Dataset Field Suitability
Assessment Module (DFSAM) 118 that identifies datasets that have a
Field Suitability Value (FSV) that exceeds a predetermined
suitability threshold value. The FSV is calculated by fields
indicated by derived metadata for a given dataset 110 against the
target data fields determined by the PDAM 112 to determine a number
of matches between the fields contained in the dataset and the
preferred target data fields. The FSV indicates a degree of
similarity between dataset fields and the target data fields that
can be measured for example, by number of class labels having
semantic similarity to the target data fields of greater than 85%
or some other value selected in accordance with the judgement of
one skilled in this field. To increase downstream computational
efficiency, the (DFSAM) 118 determines a top-k candidate datasets
that have an FSV greater that the suitability threshold value and
designates those candidate datasets as comparison datasets.
The server computer 102 includes Compared Attribute Assessment
Module (CAAM) 120 that compares the dataset metadata of the
comparison datasets identified by the (DFSAM) 118, to generate a
compared attribute score value (CASV) for each compared datasets
that represents a degree of likelihood that each associated
compared dataset has content exhibiting the target dataset
attributes. The CASVs are determined for example, by determining a
number of dataset attributes have attribute semantic similarity to
said target dataset attributes of greater than 85% (or some other
value selected in accordance with the judgement of one skilled in
this field). The server computer 102 includes a Candidate Dataset
Ranking Module (CDRM) 122 that ranks candidate dataset metadata
sets according to target attributes and generates a ranked list of
candidate datasets, indexed by the score value. It is noted that
various dataset attributes, may have different impact weights when
applied to different data use documents 108, and these various
attribute impact weights may be represented as a dataset attribute
desirability value associated with the various fields or other
attributes included in the determined metadata. The server computer
102 includes a highest ranked dataset selector that designates a
compared dataset having a highest compared attribute score value as
a selected dataset.
As an example, according to aspects of the invention, assessment of
metadata for two comparison datasets may show that dataset has
content exhibiting the target dataset attributes (e.g., "dataset
value range" and "dataset completeness"). If the "dataset value
range" attribute has a user indicated (e.g., via data use
documents) dataset attribute desirability value that is higher
(e.g., more useful to a given user) than the "dataset completeness"
attribute, then a dataset with a higher "dataset value range" score
(e.g., a broader range of values) will be ranked as more suitable
for meeting the needs and preferences of the associated user than a
dataset with a lower value range score value (e.g., a smaller range
of values). In the same example, a dataset with a higher "dataset
completeness" score, might not be ranked as more suitable, because
the "dataset completeness" attribute is not as important as
"dataset value range". In this example, having a relatively higher
score for the low-weighted "dataset completeness" attribute, as
compared to other datasets, is not enough to ensure a high ranking
for the associated dataset. However, in this example, it still
possible that the dataset associated with a relatively high
"dataset completeness" attribute score may be highly ranked by the
CDRM 122 if that dataset is shown to have a set of attribute scores
that are on average higher that is higher than an average attribute
score value of other compared datasets.
The server computer 102 also includes a Historic Use Log Update
Module (HULUM) 124 that updates a historic data field so future
uses of the selected dataset are evaluated with a higher degrees of
accuracy provided by historical context. According to aspects of
the invention, the historic use field in the metadata set
associated a dataset selected for searching with a search context
value that represent aspects of the search parameters and is
updated with search parameters each time the dataset is the
selected dataset. According to aspects of the invention, it is
noted that data and its historic utilization in different business
application can be tracked (e.g., by HULUM 124 and the selected
dataset metadata and historic use update module 126) and used to
develop rich metadata (as shown schematically at 440 in FIG. 4).
For instance, ongoing data use of business applications can be used
to identify associated domains of data use and the frequency of
utilization. This can be added back to the data sets as metadata
(e.g., via HULUM 124 and the selected dataset metadata and historic
use update module 126) and future searches can be based on this
ever expanding collection of metadata content. It is known to
search datasets using dataset content. According to aspects of the
invention, facets (e.g., Target Dataset Attributes) are including
as dataset search criteria. For example, a facet called "number of
null set entries" might capture the number of empty (e.g., null
set) fields present in each record of a given dataset. According to
aspects of the invention, having facets identified within metadata
allows a user to indicate a preference (e.g., indicate a high
attribute desirability value) for datasets exhibiting that facet.
For example, if the needs of a given user indicate a preference for
datasets with a low number of null set record entries, datasets
with relatively low numbers of null set record entries will be
ranked higher (e.g., more suitable for the user and more likely to
meet the data requirements needs and preferences of the user) by
CDRM 122 than datasets with more null set record entries. It is
also possible, according to aspects of the invention, to directly
identify certain target dataset attributes (e.g., facets) as
requirements for identification of a dataset as a selected
dataset.
Now with reference specifically to FIG. 2, and to other figures
generally, a method to rank a plurality of datasets in accordance
with dataset content and desired data attributes according to
aspects of the invention. The server computer 102 via PDAM 112 at
block 202 identifies, a set of target data fields from a set of
process documents using Diagram Identifiers, BPEL readers, and UML
assessment tools (as described above) to review and mine documents
to identify Target Data Fields.
The server computer 102 via Data Use Document Analysis Module DUDAM
114 at block 204 identifies a set of target dataset attributes from
a set of data use documents (as described above). The server
computer 102 via Metadata Generation Module (MGM) 116 generates at
block 206 a plurality of metadata sets for and associated plurality
of datasets. The server computer 102 via DFSAM 118 at block 208
determines candidate datasets having a field suitability value that
exceeds a predetermined suitability threshold value; the field
suitability value (FSV) representing a degree of similarity between
a set of fields associated with said dataset (via derived metadata
information) and the set of target data field. The server computer
102 determines, via CAAM 120 at block 210, a likelihood of each
comparison dataset exhibiting target dataset attributes.
The server computer 102 via CDRM 122 at block 212 ranks the
candidate datasets based, at least in part, on said compared
attribute score value. The server computer 102 via selected dataset
metadata and historic use update module 126 at block 214 and block
216 establishes a set of search parameters for a search to be
conducted on the selected dataset; and updates a historic use field
in the metadata set associated with the selected dataset for
searching with a search context value that represent aspects of the
search parameters. The server computer 102 via Selected Dataset
Presenter 128 at block 218 present the selected dataset 218.
According to aspects of the invention, the search context value may
be a numerical code that provides information about domains in
which particular datasets have been used previously. The search
context value may also be an unstructured text string and could
represent other previous use (including other datasets used
cooperatively) aspects for datasets provided.
Now with reference to FIG. 3A, a high level overview 310 of the
system 100 is shown. In particular, business requirements,
datasets, and metadata are provided as input, to data value engine
for processing. According to aspects of the invention, the data
value engine provides ranked datasets, data value, and facet
ranking as output. According to aspects of the invention, metadata
includes information about the data of a given dataset (e.g., for
instance, a domain associated with a given dataset). The metadata
can be stored in different forms along with the data. In many
object storage arrangements, data is stored as objects, and
metadata is stored a key-value pairs associated with the data
objects. Metadata is primarily identified (e.g., extracted with
automated mechanisms, such as analysis algorithms or similar
routines selected by one skilled in this field) from within the
data itself or manually, with input from data experts that provide
additional insight and information about the various data objects.
According to aspects of the invention, score are numerical value
that represent the relative importance of a facet (e.g., attribute
or feature) in the metadata. Facets are a data property either
derived through automation or added to the dataset as part of input
provided by a domain expert. If two datasets are available, the
dataset better for a specific requirement of an application is the
dataset with a higher score. According to aspects of the invention,
we preferably generate scores and rank datasets according to
content field attributes and the presence of preferred dataset
attributes (e.g., facets). Ranking identifies among all the
features the relative importance of a given facet for a given
dataset. Rank also determines the relative placement of various
facets when the suitability of datasets are established for the
data needs of a given user. For example, a dataset score for "null
value" attribute will indicate many null set records are in the
associated dataset. In turn, the server computer 102 will use the
score for each attribute and rank (e.g., via CDRM 122) the
attributes across various compared datasets, and within each
dataset, as well.
Now with reference to FIG. 3B, a schematic representation 320 of an
example of the system 100 in use is shown. In particular, a request
for a certain kind of information (represented by a question or a
group of questions arranged into a questionnaire, and other data
requirements) is passed to a data value engine. Several datasets
(e.g., "HR data", "customer datasets", and "Click analysis") and
associated dataset metadata are also provided to the data value
engine. The data value engine processes the input, evaluates the
provided datasets according to suitability, and provides a list of
the datasets ranked according to the determined suitability. In the
example shown, the "HR data" dataset it the top-ranked dataset,
with a determined data value of 50; the "Click analysis" dataset is
the mid-ranked dataset, with a determined data value of 46; and the
"Customer" dataset is the lowest-ranked dataset, with a determined
data value of 35.
Now with reference to FIG. 4, a schematic overview of the system
100 shown with aspects of the system arranged into multiple stages
will be discussed. In particular, a first stage 410 represents
aspects of an embodiment of the invention known collectively as
"Stage 1: Business Documentation and Process Analysis Engine," in
which BPEL documents, implementation artifacts, UML, and various
component diagrams are processed for entity and activity
extraction. A discovery unit associated with the first stage 410
includes an activity diagram locator and a class diagram locator
suited for identifying fields necessary to support system activity
in accordance with the established practices and requirements of a
given user, as represented in the process documents provided as
input. A second stage 420 represents aspects of an embodiment of
the invention known collectively as "Stage 2: Dataset Value
Evaluation Engine," in which various field requirements and desired
dataset traits, including target fields identified in first stage
410 and dataset target attributes (e.g., dataset facets) identified
in a third stage 430 (described more fully below) are compared
using known NLP, machine learning comparisons, and other methods of
computerized analysis, against sets of metadata that each describe
provided datasets. A dataset suitability value is determined for
each dataset, and the datasets are ranked according to these
values. A third stage 430 represents aspects of an embodiment of
the invention known collectively as "Stage 3: Business Interactive
Dataset Recommendation Engine," in which various business
requirement questions, associated answers, and related system
artifact mapping is passed along to the dataset evaluation engine
420 for use as described above. A fourth stage 440 represents
aspects of an embodiment of the invention known collectively as
"Stage 4: History of data usage," in which past, recorded dataset
usage and extracted metadata describing the usage are passed to the
second stage 420 for supplemental consideration when determining
dataset suitability values. In particular, the output of the fourth
stage 440 provides historic perspective and an associated increase
in score accuracy by allowing the evaluation engine of the second
stage 420 to include metadata of past dataset uses and historic
score values. This stage provides an ever-increasing perspective to
the system over multiple iterations of use, allowing the system 100
to become more accurate with increased use.
Now with reference to FIG. 5, an alternate view of the system 100
shown with aspects of the system arranged according to an exemplary
workflow outline 500. In particular, business questionnaire
information and business process information are passed from a
business owner to an activity identification phase, in which target
data facets and required system classes are identified. A business
metric to data converter then provides required facets from
business and data fields to a data field identifier, and a facet
valuation is generated. The facet valuation is passed to a dataset
valuation phase, in which a dataset ranker provides a ranking of
the datasets. This information is then passed back to the business
owner as output.
Now with reference to FIG. 6, a schematic representation 600 of
aspects of a sample embodiment of "datavalue" entry and a
"dataranking" entry generated according embodiments of the present
invention. In particular, the entries provide an indication of a
set of JSON-formatted key-value pairings useful to identify and
compare dataset values and associated dataset ranking in accordance
with aspects of the invention. It noted that other formats may be
selected in accordance with the judgement of one skilled in this
field.
Now with reference to FIG. 7, an exemplary questionnaire 700 (and
sample answers) regarding business requirements for an accounting
scenario that is assessing account attrition is shown. The server
computer 102 collects and processes answers (e.g., via DUDAM 114).
The kind of information the associated business might prefer to
gather is reflected in the answers provided in response to the
questionnaire questions. The answers provided by the user
associated with the business are used to determine target dataset
attributes.
Regarding the flowcharts and block diagrams, the flowchart and
block diagrams in the Figures of the present disclosure illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
Referring to FIG. 8, a system or computer environment 1000 includes
a computer diagram 1010 shown in the form of a generic computing
device. The method of the invention, for example, may be embodied
in a program 1060, including program instructions, embodied on a
computer readable storage device, or computer readable storage
medium, for example, generally referred to as memory 1030 and more
specifically, computer readable storage medium 1050. Such memory
and/or computer readable storage media includes non-volatile memory
or non-volatile storage. For example, memory 1030 can include
storage media 1034 such as RAM (Random Access Memory) or ROM (Read
Only Memory), and cache memory 1038. The program 1060 is executable
by the processor 1020 of the computer system 1010 (to execute
program steps, code, or program code). Additional data storage may
also be embodied as a database 1110 which includes data 1114. The
computer system 1010 and the program 1060 are generic
representations of a computer and program that may be local to a
user, or provided as a remote service (for example, as a cloud
based service), and may be provided in further examples, using a
website accessible using the communications network 1200 (e.g.,
interacting with a network, the Internet, or cloud services). It is
understood that the computer system 1010 also generically
represents herein a computer device or a computer included in a
device, such as a laptop or desktop computer, etc., or one or more
servers, alone or as part of a datacenter. The computer system can
include a network adapter/interface 1026, and an input/output (I/O)
interface(s) 1022. The I/O interface 1022 allows for input and
output of data with an external device 1074 that may be connected
to the computer system. The network adapter/interface 1026 may
provide communications between the computer system a network
generically shown as the communications network 1200.
The computer 1010 may be described in the general context of
computer system-executable instructions, such as program modules,
being executed by a computer system. Generally, program modules may
include routines, programs, objects, components, logic, data
structures, and so on that perform particular tasks or implement
particular abstract data types. The method steps and system
components and techniques may be embodied in modules of the program
1060 for performing the tasks of each of the steps of the method
and system. The modules are generically represented in the figure
as program modules 1064. The program 1060 and program modules 1064
can execute specific steps, routines, sub-routines, instructions or
code, of the program.
The method of the present disclosure can be run locally on a device
such as a mobile device, or can be run a service, for instance, on
the server 1100 which may be remote and can be accessed using the
communications network 1200. The program or executable instructions
may also be offered as a service by a provider. The computer 1010
may be practiced in a distributed cloud computing environment where
tasks are performed by remote processing devices that are linked
through a communications network 1200. In a distributed cloud
computing environment, program modules may be located in both local
and remote computer system storage media including memory storage
devices.
The computer 1010 can include a variety of computer readable media.
Such media may be any available media that is accessible by the
computer 1010 (e.g., computer system, or server), and can include
both volatile and non-volatile media, as well as, removable and
non-removable media. Computer memory 1030 can include additional
computer readable media in the form of volatile memory, such as
random access memory (RAM) 1034, and/or cache memory 1038. The
computer 1010 may further include other removable/non-removable,
volatile/non-volatile computer storage media, in one example,
portable computer readable storage media 1072. In one embodiment,
the computer readable storage medium 1050 can be provided for
reading from and writing to a non-removable, non-volatile magnetic
media. The computer readable storage medium 1050 can be embodied,
for example, as a hard drive. Additional memory and data storage
can be provided, for example, as the storage system 1110 (e.g., a
database) for storing data 1114 and communicating with the
processing unit 1020. The database can be stored on or be part of a
server 1100. Although not shown, a magnetic disk drive for reading
from and writing to a removable, non-volatile magnetic disk (e.g.,
a "floppy disk"), and an optical disk drive for reading from or
writing to a removable, non-volatile optical disk such as a CD-ROM,
DVD-ROM or other optical media can be provided. In such instances,
each can be connected to bus 1014 by one or more data media
interfaces. As will be further depicted and described below, memory
1030 may include at least one program product which can include one
or more program modules that are configured to carry out the
functions of embodiments of the present invention.
The method(s) described in the present disclosure, for example, may
be embodied in one or more computer programs, generically referred
to as a program 1060 and can be stored in memory 1030 in the
computer readable storage medium 1050. The program 1060 can include
program modules 1064. The program modules 1064 can generally carry
out functions and/or methodologies of embodiments of the invention
as described herein. The one or more programs 1060 are stored in
memory 1030 and are executable by the processing unit 1020. By way
of example, the memory 1030 may store an operating system 1052, one
or more application programs 1054, other program modules, and
program data on the computer readable storage medium 1050. It is
understood that the program 1060, and the operating system 1052 and
the application program(s) 1054 stored on the computer readable
storage medium 1050 are similarly executable by the processing unit
1020. It is also understood that the application 1054 and
program(s) 1060 are shown generically, and can include all of, or
be part of, one or more applications and program discussed in the
present disclosure, or vice versa, that is, the application 1054
and program 1060 can be all or part of one or more applications or
programs which are discussed in the present disclosure
One or more programs can be stored in one or more computer readable
storage media such that a program is embodied and/or encoded in a
computer readable storage medium. In one example, the stored
program can include program instructions for execution by a
processor, or a computer system having a processor, to perform a
method or cause the computer system to perform one or more
functions.
The computer 1010 may also communicate with one or more external
devices 1074 such as a keyboard, a pointing device, a display 1080,
etc.; one or more devices that enable a user to interact with the
computer 1010; and/or any devices (e.g., network card, modem, etc.)
that enables the computer 1010 to communicate with one or more
other computing devices. Such communication can occur via the
Input/Output (I/O) interfaces 1022. Still yet, the computer 1010
can communicate with one or more networks 1200 such as a local area
network (LAN), a general wide area network (WAN), and/or a public
network (e.g., the Internet) via network adapter/interface 1026. As
depicted, network adapter 1026 communicates with the other
components of the computer 1010 via bus 1014. It should be
understood that although not shown, other hardware and/or software
components could be used in conjunction with the computer 1010.
Examples, include, but are not limited to: microcode, device
drivers 1024, redundant processing units, external disk drive
arrays, RAID systems, tape drives, and data archival storage
systems, etc.
It is understood that a computer or a program running on the
computer 1010 may communicate with a server, embodied as the server
1100, via one or more communications networks, embodied as the
communications network 1200. The communications network 1200 may
include transmission media and network links which include, for
example, wireless, wired, or optical fiber, and routers, firewalls,
switches, and gateway computers. The communications network may
include connections, such as wire, wireless communication links, or
fiber optic cables. A communications network may represent a
worldwide collection of networks and gateways, such as the
Internet, that use various protocols to communicate with one
another, such as Lightweight Directory Access Protocol (LDAP),
Transport Control Protocol/Internet Protocol (TCP/IP), Hypertext
Transport Protocol (HTTP), Wireless Application Protocol (WAP),
etc. A network may also include a number of different types of
networks, such as, for example, an intranet, a local area network
(LAN), or a wide area network (WAN).
In one example, a computer can use a network which may access a
website on the Web (World Wide Web) using the Internet. In one
embodiment, a computer 1010, including a mobile device, can use a
communications system or network 1200 which can include the
Internet, or a public switched telephone network (PSTN) for
example, a cellular network. The PSTN may include telephone lines,
fiber optic cables, transmission links, cellular networks, and
communications satellites. The Internet may facilitate numerous
searching and texting techniques, for example, using a cell phone
or laptop computer to send queries to search engines via text
messages (SMS), Multimedia Messaging Service (MMS) (related to
SMS), email, or a web browser. The search engine can retrieve
search results, that is, links to websites, documents, or other
downloadable data that correspond to the query, and similarly,
provide the search results to the user via the device as, for
example, a web page of search results.
The present invention may be a system, a method, and/or a computer
program product at any possible technical detail level of
integration. The computer program product may include a computer
readable storage medium (or media) having computer readable program
instructions thereon for causing a processor to carry out aspects
of the present invention.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects of the present
invention.
Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
These computer readable program instructions may be provided to a
processor of a computer, or other programmable data processing
apparatus to produce a machine, such that the instructions, which
execute via the processor of the computer or other programmable
data processing apparatus, create means for implementing the
functions/acts specified in the flowchart and/or block diagram
block or blocks. These computer readable program instructions may
also be stored in a computer readable storage medium that can
direct a computer, a programmable data processing apparatus, and/or
other devices to function in a particular manner, such that the
computer readable storage medium having instructions stored therein
comprises an article of manufacture including instructions which
implement aspects of the function/act specified in the flowchart
and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the blocks may occur out of the order noted in
the Figures. For example, two blocks shown in succession may, in
fact, be accomplished as one step, executed concurrently,
substantially concurrently, in a partially or wholly temporally
overlapping manner, or the blocks may sometimes be executed in the
reverse order, depending upon the functionality involved. It will
also be noted that each block of the block diagrams and/or
flowchart illustration, and combinations of blocks in the block
diagrams and/or flowchart illustration, can be implemented by
special purpose hardware-based systems that perform the specified
functions or acts or carry out combinations of special purpose
hardware and computer instructions.
It is to be understood that although this disclosure includes a
detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, embodiments of the present invention are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, network
bandwidth, servers, processing, memory, storage, applications,
virtual machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported, providing transparency
for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
Referring now to FIG. 9, illustrative cloud computing environment
2050 is depicted. As shown, cloud computing environment 2050
includes one or more cloud computing nodes 2010 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 2054A,
desktop computer 2054B, laptop computer 2054C, and/or automobile
computer system 2054N may communicate. Nodes 2010 may communicate
with one another. They may be grouped (not shown) physically or
virtually, in one or more networks, such as Private, Community,
Public, or Hybrid clouds as described hereinabove, or a combination
thereof. This allows cloud computing environment 2050 to offer
infrastructure, platforms and/or software as services for which a
cloud consumer does not need to maintain resources on a local
computing device. It is understood that the types of computing
devices 2054A-N shown in FIG. 9 are intended to be illustrative
only and that computing nodes 2010 and cloud computing environment
2050 can communicate with any type of computerized device over any
type of network and/or network addressable connection (e.g., using
a web browser).
Referring now to FIG. 10, a set of functional abstraction layers
provided by cloud computing environment 2050 (FIG. 9) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 10 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Hardware and software layer 2060 includes hardware and software
components. Examples of hardware components include: mainframes
2061; RISC (Reduced Instruction Set Computer) architecture based
servers 2062; servers 2063; blade servers 2064; storage devices
2065; and networks and networking components 2066. In some
embodiments, software components include network application server
software 2067 and database software 2068.
Virtualization layer 2070 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 2071; virtual storage 2072; virtual networks 2073,
including virtual private networks; virtual applications and
operating systems 2074; and virtual clients 2075.
In one example, management layer 2080 may provide the functions
described below. Resource provisioning 2081 provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and Pricing 2082 provide cost tracking as resources are
utilized within the cloud computing environment, and billing or
invoicing for consumption of these resources. In one example, these
resources may include application software licenses. Security
provides identity verification for cloud consumers and tasks, as
well as protection for data and other resources. User portal 2083
provides access to the cloud computing environment for consumers
and system administrators. Service level management 2084 provides
cloud computing resource allocation and management such that
required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 2085 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
Workloads layer 2090 provides examples of functionality for which
the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation 2091; software development and
lifecycle management 2092; virtual classroom education delivery
2093; data analytics processing 2094; transaction processing 2095;
and an automatic method to rank a plurality of datasets in
accordance with dataset content and desired data attributes
2096.
The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Likewise, examples of features or functionality of the
embodiments of the disclosure described herein, whether used in the
description of a particular embodiment, or listed as examples, are
not intended to limit the embodiments of the disclosure described
herein, or limit the disclosure to the examples described herein.
Many modifications and variations will be apparent to those of
ordinary skill in the art without departing from the scope and
spirit of the described embodiments. The terminology used herein
was chosen to best explain the principles of the embodiments, the
practical application or technical improvement over technologies
found in the marketplace, or to enable others of ordinary skill in
the art to understand the embodiments disclosed herein.
* * * * *
References